Citation entity recognition method using multi‐feature semantic fusion based on deep learning

Abstract

The effective entity recognition method can quickly and accurately identify the citation entity to facilitate citation comparison, thereby reducing the occurrence of academic fraud and other behaviors. But there is no very effective way to solve this problem till now. In recent years, neural network models for named entity recognition (NER) have shown better performances on general domain datasets. After the multi-feature citation dataset is created, the article proposes contextual multi-feature embedding (CMFE) method for word embedding which use multi-feature to enhance semantic and use CNN to get multi-level feature. Based on CMFE, a multi-feature semantic fusion model (MFSFM) is proposed. It designs the multi-convolution kernel mixed residual CNN module to obtain local attention information and enhance the sensitivity of the entity boundary information. The BiLSTM and LSTM is used for timing learning. The experimental results of Chinese citation datasets and Chinese-English mixed citation datasets show that CMFE can better represent semantics, and MFSFM can perform citation entity recognition well. Finally, the experimental results of CONLL2003 dataset show that it is general on NER.

A Multigranularity Text Driven Named Entity Recognition CGAN Model for Traditional Chinese Medicine Literatures

“…Zhang and Yang [ 20 ] designed a Lattice-LSTM model to encode characters and words, respectively, which achieved the utilization of information on both words and order of words and avoided the problem of word separation errors. Gao et al [ 21 ] proposed contextual multifeature embedding (CMFE) and used it to construct a multifeature semantic fusion model (MFSFM) to realize citation entity recognition. Sun [ 22 ] referred to a method of recognizing Chinese clinically named entities based on multistrategy fusion.…”

Research on Named Entity Recognition Method of Metro On-Board Equipment Based on Multiheaded Self-Attention Mechanism and CNN-BiLSTM-CRF

“…Literature [ 17 , 18 ] proposes that CRF is used as the processing mode of output processing layer on the basis of bidirectional LSTM, effectively improving the performance of the model. Furthermore, convolutional neural network (CNN) [ 19 ] has also achieved desirable results in solving NER problems; literature [ 20 ] uses CNN to obtain multilevel features, thereby yielding local attention information and improving the sensitivity of entity boundary information; literature [ 21 ] adopts the serial strategy of CNN and LSTM-CRF to recognize the named entity of the conll2003 English dataset, and obtains a higher F1 value. However, LSTM network cannot capture text information in both directions.…”